In this work, we use the term robustness of the explanation method as a notion of resilience against adversarial attacks, i.e. as the ability of an explanation method to recognize the biased classifier in an adversary environment. This type of robustness could be more formally defined as the number of instances where the adversarial model's bias was correctly recognized. We focus on the robustness concerning the attacks described in [1]}. There are other notions of robustness in explanation methods; e.g., [2]} define the robustness of the explanations in the sense that similar inputs should give rise to similar explanations.
